CCPlugins

🥇Gold

CCPlugins is a Python framework that automates Claude Code interactions for operations teams. It replaces repetitive prompts with CLI commands, saving time and improving efficiency. Integrates with Claude's native capabilities and CLI.

2,6581590Updated 1mo ago

Intermediate30min to implementautomation

Saves ~60 min per use

Quick InstallView Source

git clone https://github.com/brennercruvinel/CCPlugins.git

Works with:

Claude

Overview

About This Skill

https://github.com/brennercruvinel/CCPlugins

Use Cases

Automatically clean up a project by removing unnecessary debug files and backups.

Generate complete features from predefined patterns to accelerate development.

Conduct multi-agent security and quality reviews to ensure code integrity.

Convert TODO comments in the code into actionable GitHub issues for better task management.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/brennercruvinel/CCPlugins

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Act as a senior engineer using the CCPlugins framework. I'm working on a [PROJECT_TYPE] for [COMPANY] in the [INDUSTRY] sector. Here's the [DATA] I need to process. Please use the appropriate CCPlugins to [SPECIFIC_TASK].

Example Output

# Project Analysis for GreenTech Solutions

## Project Overview
- **Project Type**: Web Application
- **Industry**: Renewable Energy
- **Key Data**: User engagement metrics, server response times, API latency

## Recommended CCPlugins
1. **DataParser**: Extract and format key metrics from raw data
2. **PerformanceAnalyzer**: Identify bottlenecks in server response times
3. **APIOptimizer**: Suggest improvements for API latency

## Action Plan
1. Run DataParser on the provided user engagement metrics
2. Analyze server response times with PerformanceAnalyzer
3. Optimize API endpoints using APIOptimizer
4. Generate a comprehensive report with recommendations

## Expected Outcome
- Improved user experience through optimized performance
- Reduced server load and faster response times
- Enhanced API efficiency and reliability